The Predicate Calculus

1
The Predicate Calculus
2
2.0 Introduction 2.1 The Propositional
Calculus 2.2 The Predicate Calculus 2.3 Using
Inference Rules to Produce Predicate Calculus
Expressions
2.4 Application A Logic- Based Financial
Advisor 2.5 Epilogue and References 2.6 Exercis
es
Additional references for the slides Robert
Wilenskys CS188 slides www.cs.berkeley.edu/7wil
ensky/cs188/lectures/index.html
2
Chapter Objectives

• Learn the basics of knowledge representation
• Learn the basics of inference using
  propositional logic and predicate logic
• The agent model Has a knowledge base of logical
  statements and can draw inferences.

3
Knowledge Representation (KR)

• Given the world
• Express the general facts or beliefs using a
  language
• Determine what else we should (not) believe

4
Example

• Given
• The red block is above the blue block
• The green block is above the red block
• Infer
• The green block is above the blue block
• The blocks form a tower

5
Example

• Given
• If it is sunny today, then the sun shines on
  the screen. If the sun shines on the screen, the
  blinds are brought down. The blinds are not
  down.
• Find out
• Is it sunny today?

6
A KR language needs to be

• expressive
• unambiguous
• flexible

7
The inference procedures need to be

• Correct (sound)
• Complete
• Efficient

8
Candidates (for now)

• English (natural language)
• Java (programming language)
• Logic (special KR language)

9
Logic consists of

• A language which tells us how to build up
  sentences in the language (i.e., syntax),
  and and what the sentences mean (i.e.,
  semantics)
• An inference procedure which tells us which
  sentences are valid inferences from other
  sentences

10
Propositional logic

• The symbols of propositional calculus are
• the propositional symbols
• P, Q, R, S,
• the truth symbols
• true, false
• and connectives
• ?, ?, ?, ?, ?

11
Propositional Calculus Sentences

• Every propositional symbol and truth symbol is a
  sentence.
• Examples true, P, Q, R.
• The negation of a sentence is a sentence.
• Examples ?P, ? false.
• The conjunction, or and, of two sentences is a
  sentence.
• Example P ? ?P

12
Propositional Calculus Sentences (contd)

• The disjunction, or or, of two sentences is a
  sentence.
• Example P ? ?P
• The implication of one sentence from another is a
  sentence.
• Example P ? Q
• The equivalence of two sentences is a sentence.
• Example P ? Q ? R
• Legal sentences are also called well-formed
  formulas or WFFs.

13
Propositional calculus semantics

• An interpretation of a set of propositions is the
  assignment of a truth value, either T or F to
  each propositional symbol.
• The symbol true is always assigned T, and the
  symbol false is assigned F.
• The truth assignment of negation, ?P, where P is
  any propositional symbol, is F if the assignment
  to P is T, and is T is the assignment to P is F.
• The truth assignment of conjunction, ?, is T only
  when both conjuncts have truth value T otherwise
  it is F.

14
Propositional calculus semantics (contd)

• The truth assignment of disjunction, ?, is F only
  when both disjuncts have truth value F otherwise
  it is T.
• The truth assignment of implication, ?, is F only
  when the premise or symbol before the implication
  is T and the truth value of the consequent or
  symbol after the implication F otherwise it is
  T.
• The truth assignment of equivalence, ?, is T only
  when both expressions have the same truth
  assignment for all possible interpretations
  otherwise it is F.

15
For propositional expressions P, Q, R
16
Fig. 2.1 Truth table for the operator ?
17
Fig. 2.2 Truth table demonstrating the
equivalence of ?P?Q and P?Q
18
Proofs in propositional calculus

• If it is sunny today, then the sun shines on the
  screen. If the sun shines on the screen, the
  blinds are brought down. The blinds are not down.
• Is it sunny today?
• P It is sunny today.
• Q The sun shines on the screen.
• R The blinds are down.
• Premises P?Q, Q?R, ?R
• Question P

19
Prove using a truth table
20
Propositional calculus is cumbersome

• If it is sunny today, then the sun shines on the
  screen. If the sun shines on the screen, the
  blinds are brought down. The blinds are not down.
• Is it sunny today?
• - - -
• If it is sunny on a particular day, then the sun
  shines on the screen. If the sun shines on the
  screen on a particular day, the blinds are
  brought down. The blinds are not down today.
• Is it sunny today?

21
Represent in predicate calculus

• If it is sunny on a particular day, then the sun
  shines on the screen on that day. If the sun
  shines on the screen on a particular day, the
  blinds are down on that day.The blinds are not
  down today.
• Is it sunny today?
• Premises
• ?D sunny (D)? screen-shines (D)
• ?D screen-shines(D) ? blinds-down(D)
• ? blinds-down (today)
• Question sunny(today)

22
Can also use functions

• A persons mother is that persons parent.
• ?X person (X)? parent(mother-of(X),X)
• There are people who think this class is cool.
• ?X person (X) ? T (X)
• Some computers have mouses connected on the USB.
• ? X computer (X) ? USB_conn (X, mouse_of(X))

23
Predicate calculus symbols

• The set of letters (both uppercase and
  lowercase) A Z, a Z.
• The set of digits 0 9
• The underscore _
• Needs to start with a letter.

24
Symbols and terms

• 1. Truth symbols true and false (these are
  reserved symbols)
• 2. Constant symbols are symbol expressions having
  the first character lowercase.
• E.g., today, fisher
• 3. Variable symbols are symbol expressions
  beginning with an uppercase character.
• E.g., X, Y, Z, Building
• 4. Function symbols are symbol expressions having
  the first character lowercase. Arity number of
  elements in the domain
• E.g., mother-of (bill) maximum-of (7,8)

25
Symbols and terms (contd)

• A function expression consists of a function
  constant of arity n, followed by n terms, t1 ,t2
  ,, tn, enclosed in parentheses and separated by
  commas.
• E.g., mother-of(mother-of(joe))
• maximum(maximum(7, 18), add_one(18))
• A term is either a constant, variable, or
  function expression.
• E.g. color_of(house_of(neighbor(joe)))
• house_of(X)

26
Predicates and atomic sentences

• Predicate symbols are symbols beginning with a
  lowercase letter. Predicates are special
  functions with true/false as the range.Arity
  number of arguments
• An atomic sentence is a predicate constant of
  arity n, followed by n terms, t1 ,t2 ,, tn,
  enclosed in parentheses and separated by commas.
• The truth values, true and false, are also atomic
  sentences.

27
Examples

• greater_than(2, 3)
• mother_of(joe, susan)
• mother_of(sister_of(joe), susan)

Predicate symbol
term (constant)
28
Predicate calculus sentences

• Every atomic sentence is a sentence.
• 1. If s is a sentence, then so is its negation,
  ?s.
• If s1 and s2 are sentences, then so is their
• 2. Conjunction, s1 ? s2 .
• 3. Disjunction, s1 ? s2 .
• 4. Implication, s1 ? s2 .
• 5. Equivalence, s1 ? s2 .

29
Predicate calculus sentences (contd)

• If X is a variable and s is a sentence, then so
  are
• 6. ?X s.
• 7. ?X s.
• Remember that logic sentences evaluate to true or
  false, therefore only such objects are atomic
  sentences. Functions are not atomic sentences.

30
verify_sentence algorithm
31
A logic-based Knowledge Base (KB)
Contains Facts (quantified or not) Function imp
lementations
Add more facts
Delete existing facts
Result
Pose queries
32
Interpretation

• Let the domain D be a nonempty set.
• An interpretation over D is an assignment of the
  entities of D to each of the constant, variable,
  predicate, and function symbols of a predicate
  calculus expression
• 1. Each constant is assigned an element of D.
• 2. Each variable is assigned to a nonempty subset
  of D (allowable substitutions).
• 3. Each function f of arity m is defined (Dm to
  D).
• 4. Each predicate of arity n is defined (Dn to
  T,F).

33
How to compute the truth value of predicate
calculus expressions

• Assume an expression E and an interpretation I
  over E over a nonempty domain D. The truth value
  for E is determined by
• 1. The value of a constant is the element of D it
  is assigned to by I.
• 2. The value of a variable is the set of elements
  of D it is assigned to by I.
• 3. The value of a function expression is that
  element of D obtained by evaluating the function
  for the parameter values assigned by the
  interpretation.

34
How to compute the truth value of predicate
calculus expressions (contd)

• 4. The value of the truth symbol true is T, and
  false is F.
• 5. The value of an atomic sentence is either T or
  F, as determined by the interpretation I.
• 6. The value of the negation of a sentence is T
  if the value of the sentence is F, and F if the
  value of the sentence is T.
• 7. The value of the conjunction of two sentences
  is T, if the value of both sentences is T and F
  otherwise.
• 8-10. The truth value of expressions using ?,?,
  and ? is determined as defined in Section 2.1.2.

35
How to compute the truth value of predicate
calculus expressions (contd)

• Finally, for a variable X and a sentence S
  containing X
• 11. The value of ?X S is T if S is T for all
  assignments to X under I, and it is F otherwise.
• 12. The value of ?X S is T if there is an
  assignment to X under I such that S is T, and it
  is F otherwise

36
Revisit ? and ?

• A persons mother is that persons parent.
• ?X person (X)? parent(mother-of(X),X)
• vs.
• ?X person (X) ? parent(mother-of(X),X)
• I joe, jane are people
• fido is a dog
• person (joe) is T, person (jane) is T
• person (fido) is F, dog (fido) is T
• mother-of (joe) is jane

37
Revisit ? and ? (contd)

• There are people who think this class is cool.
• ?X person (X) ? T (X)
• vs.
• ?X person (X) ? T (X)
• I joe, jane are people
• fido is a dog
• person (joe) is T, person (jane) is T
• person (fido) is F, dog (fido) is T
• mother-of (joe) is jane

38
First-order predicate calculus

• First-order predicate calculus allows quantified
  variables to refer to objects in the domain of
  discourse and not to predicates or functions.
• John likes to eat everything.
• ?X food(X) ? likes (john,X)
• John likes at least one dish Jane likes.
• ?F food(F) ? likes (jane, F) ? likes (john, F)
  
• John does everything Jane does.
• ?P P(Jane) ? P(john) This is not first-order.

39
Order of quantifiers matters

• Everybody likes some food.
• There is a food that everyone likes.
• Whenever someone likes at least one spicy dish,
  theyre happy.

40
Order of quantifiers matters

• Everybody likes some food.
• ?X ?F food(F) ? likes (X,F)
• There is a food that everyone likes.
• ?F ?X food(F) ? likes (X,F)
• Whenever someone eats a spicy dish, theyre
  happy.
• ?X ?F food(F) ? spicy(F) ? eats (X,F) ?
• happy(X)

41
Examples

• Johns meals are spicy.
• Every city has a dogcatcher who has been bitten
  by every dog in town.
• For every set x, there is a set y, such that the
  cardinality of y is greater than the cardinality
  of x.

42
Examples

• Johns meals are spicy.
• ?X meal-of(John,X) ? spicy(X)
• Every city has a dogcatcher who has been bitten
  by every dog in town.
• ?T ?C ?D city(C) ? ( dogcatcher(C,T) ?
• (dog(D) ? lives-in (D, T) ? bit (D, C)) )

43
Examples (contd)

• For every set x, there is a set y, such that the
  cardinality of y is greater than the cardinality
  of x.
• ?X ?Y ?U ?V set(X) ? (set(Y) ? cardinality(X,U)
• ? cardinality(Y, V) ? greater-than(V,U))

44
The role of the knowledge engineer

• fisher-hall-is-a-building
• ee-is-a-building
• building (fisher)
• building (ee)
• white-house-on-the-corner-is-a-building
• green (fisher)
• color (fisher, green)
• holds (color, fisher, green)
• holds (color, fisher, green, jan-2003)
• holds (color, fisher, blue, jul-2003)

45
Blocks world

• on (c,a)
• on(b,d)
• ontable(a)
• ontable(d)
• clear(b)
• clear(c)
• hand_empty

c
b
a
d
46
Blocks world example

• All blocks on top of blocks that have been moved
  or that are attached to blocks that have been
  moved have also been moved.
• ?X ?Y (block(X) ? block(Y) ?
• (on(X,Y) ? attached (X,Y)) ? moved (Y)) ?
• moved(X)

47
Satisfy, model, valid, inconsistent

• For a predicate calculus expression X and an
  interpretation I
• If X has a value of T under I and a particular
  variable assignment, then I is said to satisfy X.
• If I satisfies X for all variable assignments,
  then I is a model of X.
• X is satisfiable iff there is an interpretation
  and variable assignment that satisfy it
  otherwise it is unsatisfiable.

48
Satisfy, model, valid, inconsistent (contd)

• A set of expressions is satisfiable iff there is
  an interpretation and variable assignment that
  satisfy every element.
• If a set of expressions is not satisfiable, it is
  said to be inconsistent.
• If X has a value T for all possible
  interpretations, X is said to be valid.

49
Proof procedure

• A proof procedure is a combination of an
  inference rule and an algorithm for applying that
  rule to a set of logical expressions to generate
  new sentences.
• (Proof by resolution inference rule is described
  in Chapter 13.)

50
Logically follows, sound, and complete

• A predicate calculus expression X logically
  follows from a set S of predicate calculus
  expressions if every interpretation and variable
  assignment that satisfies S also satisfies X.
• An inference rule is sound if every predicate
  calculus expression produced by the rule from a
  set S of predicate calculus expressions also
  logically follows from S.
• An inference rule is complete if, given a set S
  of predicate calculus expressions, the rule can
  infer every expression that logically follows
  from S.

51
Modus ponens and modus tollens

• If the sentences P and P ? Q are known to be
  true, then modus ponens lets us infer Q.
• If the sentence P ? Q is known to be true, and
  the sentence Q is known to be false, modus
  tollens lets us infer ?P.

52
And elimination / and introduction

• And elimination lets us infer the truth of either
  of the conjuncts from the truth of a conjunctive
  sentence. For instance, P ? Q lets us conclude
  both P and Q are true.
• And introduction allows us to infer the truth of
  a conjunction from the truth of its conjuncts.
  For instance, if P and Q are true, then P ? Q is
  true.

53
Universal instantiation

• Universal instantion states that if any
  universally quantified variable in a true
  sentence is replaced by any appropriate term from
  the domain, the result is a true sentence. Thus,
  if a is from the domain of X,? X P(X) lets us
  infer P(a).

54
Revisit the sunny day example

• ?D sunny (D)? screen-shines (D)
• ?D screen-shines (D) ? blinds-down (D)
• ? blinds-down (today)
• Question sunny (today)
• Use unification and modus tollens
• sunny (today) ? screen-shines (today)
• screen-shines (today)? blinds-down (today)
• ? blinds-down (today)

55
Unification

• Make sentences look alike.
• Unify p(a,X) and p(a,b)
• Unify p(a,X) and p(Y,b)
• Unify p(a,X) and p(Y, f(Y))
• Unify p(a,X) and p(X,b)
• Unify p(a,X) and p(Y,b)
• Unify p(a,b) and p(X, X)

56
Unification examples

• Unify p(a,X) and p(a,b)
• answer b/X p(a,b)
• Unify p(a,X) and p(Y,b)
• answer a/Y, b/X p(a,b)
• Unify p(a,X) and p(Y, f(Y))
• answer a/Y, f(a)/X p(a,f(a))

57
Unification examples (contd)

• Unify p(a,X) and p(X,b)
• failure
• Unify p(a,X) and p(Y,b)
• answer a/Y, b/X p(a,b)
• Unify p(a,b) and p(X,X)
• failure
• Unify p(X, f(Y), b) and P(X, f(b), b)
• answer b/Y this is an mgu
• b/X, b/Y this in not an mgu

58
Most general unifier (mgu)

• If s is any unifier of expressions E and g is the
  most general unifier of that set of expressions,
  then for s applied to E there exists another
  unifier s such that Es Egs, where Es and Egs
  are the composition of unifiers applied to the
  expression E.
• Basic idea Commit to a substitution only if you
  have to keep it as general as possible.

59
Unification algorithm

• Basic idea can replace variables by
• other variables
• constants
• function expressions
• High level algorithm
• Represent the expression as a list
• Process the list one by one
• Determine a substitution (if necessary)
• Apply to the rest of the list before proceeding

60
Examples with the algorithm

• Unify p(a,X) and p(a,b)
• (p a X) (p a b)
• Unify p(a,X) and p(Y, f(Y))
• (p a X) (p Y (f Y))
• Unify parents(X, father(X), mother(bill)) and
• parents(bill, father(bill), Y)
• (parents X (father X) (mother bill))
• (parents bill (father bill) Y)

61
function unify code
62
The books example
63
Processed example

• (parents X (father X) (mother bill)), (parents
  bill (father bill) Y)
• parents ? parents yes
• return nil
• (X (father X) (mother bill)), (bill (father bill)
  Y)
• X ? bill no, substitute
• return bill/X
• (bill (father bill) (mother bill)), (bill (father
  bill) Y)
• bill ? bill yes
• return nil

64
Processed example (contd)

• ( (father bill) (mother bill)), ( (father bill)
  Y)
• (father bill), (father bill)
• father ? father yes
• return nil
• (bill) (bill)
• bill ? bill yes
• return nil

65
Processed example (contd)

• (mother bill), Y
• (mother bill) ? Y no, substitute
• return (mother bill) / Y
• The set of unifying substitutions for
• (parents X (father X) (mother bill)), (parents
  bill (father bill) Y)
• is
• bill / X, (mother bill) / Y.
• The result is
• (parents bill (father bill) (mother bill))

66
A Logic-Based Financial Advisor

• Gives investment advice (savings account, or the
  stock market, or both).
• Example rule
• If the amount in the savings account is
  inadequate, increasing this amount should be the
  first priority.

67
Sentences

• 1. savings_account (inadequate) ?
  investment(savings)
• 2. savings_account (adequate) ?
  income(adequate) ? investment (stocks)
• 3. savings_account (adequate) ?
  income(inadequate) ? investment (combination)
• 4. ? X amount_saved(X) ? ? Y (dependents (Y) ?
  greater(X, minsavings(Y))) ?
  savings_account(adequate)
• Y is the number of dependents, minsavings is
  the number of dependents multiplied by 5000.

68
Sentences (contd)

• 5. ? X amount_saved(X) ? ? Y (dependents (Y) ?
  ? greater (X, minsavings(Y))) ?
  savings_account(inadequate)
• 6. ? X earnings(X, steady) ? ? Y (dependents (Y)
  ? greater (X, minincome(Y))) ?
  income(adequate)
• 7. ? X earnings(X, steady) ? ? Y (dependents (Y)
  ? ? greater (X, minincome(Y))) ?
  income(inadequate)
• Minimum income is 15,000 (4000 number
  of dependents)
• 8. ? X earnings(X, unsteady) ? income(inadequate)

69
Sentences (contd)

• 9. amount_saved(22000)
• 10. earnings(25000, steady)
• 11. dependents (3)
• The knowledge base is an implicit ? of the
  sentences above.
• Using 10, 11, and 7 we can infer
• 12. income(inadequate)
• Using 9, 11, and 4, we can infer
• 13. savings_account(adequate)
• Using 12, 13, and 3, we can infer
• 14. investment(combination)

70
Summary

• Propositional calculus no variables or functions
• Predicate calculus allows quantified variables
  as parameters of predicates or functions
• Higher order logics allows predicates to be
  variables (might be needed to describe
  mathematical properties such as every
  proposition implies itself or there are
  decidable propositions.)

71
Key concepts

• Sentence
• Interpretation
• Proposition, term, function, atom
• Unification and mgu
• Proofs in logic